A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

• Published in: Eng (Basel, Switzerland) Switzerland), 2023-09, Vol.4 (3), p.1787-1796
• Main Authors: Santos, Leonardo B. L., Freitas, Cintia P., Bacelar, Luiz, Soares, Jaqueline A. J. P., Diniz, Michael M., Lima, Glauston R. T., Stephany, Stephan
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Concentration time; Flash floods; Flood predictions; Floods; High resolution; Hydrologic cycle; Hydrologic models; hydrologic prediction; hydrological prediction; Hydrology; Hydrometeorology; Machine learning; Mathematical models; Meteorological radar; Moisture effects; Mountainous areas; Neural networks; Neurons; Radar data; Rain; Rainfall; Random numbers; River networks; Soil moisture; Water levels; Watersheds; Weather forecasting; weather radar
• ISSN: 2673-4117; 2673-4117
• DOI: 10.3390/eng4030101

Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.